In [1]:
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

import calendar
In [2]:
import datetime as dt
import plotly.io as pio
pio.templates
Out[2]:
Templates configuration
-----------------------
    Default template: 'plotly'
    Available templates:
        ['ggplot2', 'seaborn', 'simple_white', 'plotly',
         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
         'ygridoff', 'gridon', 'none']
In [3]:
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from IPython.display import HTML
In [4]:
df = pd.read_csv(r'C:\Users\supritha\Documents\Unemployment.csv')
df.head()
Out[4]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.74
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.74
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.74
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.74
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.74
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB
In [6]:
df.isnull().sum()
Out[6]:
Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64
In [7]:
df.columns =['States','Date','Frequency','Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate','Region','longitude','latitude']
In [8]:
df['Date'] = pd.to_datetime(df['Date'],dayfirst=True)
In [9]:
df['Frequency']= df['Frequency'].astype('category')
In [10]:
df['Month'] =  df['Date'].dt.month
In [11]:
df['Month_int'] = df['Month'].apply(lambda x : int(x))
In [12]:
df['Month_name'] =  df['Month_int'].apply(lambda x: calendar.month_abbr[x])
In [13]:
df['Region'] = df['Region'].astype('category')
In [14]:
df.drop(columns='Month',inplace=True)
df.head(3)
Out[14]:
States Date Frequency Estimated Unemployment Rate Estimated Employed Estimated Labour Participation Rate Region longitude latitude Month_int Month_name
0 Andhra Pradesh 2020-01-31 M 5.48 16635535 41.02 South 15.9129 79.74 1 Jan
1 Andhra Pradesh 2020-02-29 M 5.83 16545652 40.90 South 15.9129 79.74 2 Feb
2 Andhra Pradesh 2020-03-31 M 5.79 15881197 39.18 South 15.9129 79.74 3 Mar
In [15]:
df_stats = df[['Estimated Unemployment Rate',
       'Estimated Employed', 'Estimated Labour Participation Rate']]


round(df_stats.describe().T,2)
Out[15]:
count mean std min 25% 50% 75% max
Estimated Unemployment Rate 267.0 12.24 10.80 0.50 4.84 9.65 16.76 75.85
Estimated Employed 267.0 13962105.72 13366318.36 117542.00 2838930.50 9732417.00 21878686.00 59433759.00
Estimated Labour Participation Rate 267.0 41.68 7.85 16.77 37.26 40.39 44.06 69.69
In [16]:
region_stats = df.groupby(['Region'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean().reset_index()

region_stats = round(region_stats,2)


region_stats
Out[16]:
Region Estimated Unemployment Rate Estimated Employed Estimated Labour Participation Rate
0 East 13.92 19602366.90 40.11
1 North 15.89 13072487.92 38.70
2 Northeast 10.95 3617105.53 52.06
3 South 10.45 14040589.33 40.44
4 West 8.24 18623512.72 41.26
In [17]:
heat_maps = df[['Estimated Unemployment Rate',
       'Estimated Employed', 'Estimated Labour Participation Rate',
       'longitude', 'latitude', 'Month_int']]

heat_maps = heat_maps.corr()

plt.figure(figsize=(10,6))
sns.set_context('notebook',font_scale=1)
sns.heatmap(heat_maps, annot=True,cmap='summer');
In [18]:
fig = px.box(df,x='States',y='Estimated Unemployment Rate',color='States',title='Unemployment rate',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()
In [19]:
fig = px.scatter_matrix(df,template='plotly',
    dimensions=['Estimated Unemployment Rate','Estimated Employed',
                'Estimated Labour Participation Rate'],
    color='Region')
fig.show()
In [20]:
plot_ump = df[['Estimated Unemployment Rate','States']]

df_unemp = plot_ump.groupby('States').mean().reset_index()

df_unemp = df_unemp.sort_values('Estimated Unemployment Rate')

fig = px.bar(df_unemp, x='States',y='Estimated Unemployment Rate',color='States',
            title='Average Unemployment Rate in each state',template='plotly')

fig.show()
In [21]:
fig = px.bar(df, x='Region',y='Estimated Unemployment Rate',animation_frame = 'Month_name',color='States',
            title='Unemployment rate across region from Jan.2020 to Oct.2020', height=700,template='plotly')

fig.update_layout(xaxis={'categoryorder':'total descending'})

fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000

fig.show()
In [23]:
unemplo_df = df[['States','Region','Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']]

unemplo = unemplo_df.groupby(['Region','States'])['Estimated Unemployment Rate'].mean().reset_index()
In [24]:
fig = px.sunburst(unemplo, path=['Region','States'], values='Estimated Unemployment Rate',
                  color_continuous_scale='Plasma',title= 'unemployment rate in each region and state',
                  height=650,template='ggplot2')


fig.show()

D:\juypiter\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

D:\juypiter\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
In [25]:
fig = px.scatter_geo(df,'longitude', 'latitude', color="Region",
                     hover_name="States", size="Estimated Unemployment Rate",
                     animation_frame="Month_name",scope='asia',template='plotly',title='Impack of lockdown on employement across regions')

fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000

fig.update_geos(lataxis_range=[5,35], lonaxis_range=[65, 100],oceancolor="#6dd5ed",
    showocean=True)

fig.show()
In [26]:
lock = df[(df['Month_int'] >= 4) & (df['Month_int'] <=7)]

bf_lock = df[(df['Month_int'] >= 1) & (df['Month_int'] <=4)]
In [27]:
g_lock = lock.groupby('States')['Estimated Unemployment Rate'].mean().reset_index()

g_bf_lock = bf_lock.groupby('States')['Estimated Unemployment Rate'].mean().reset_index()


g_lock['Unemployment Rate before lockdown'] = g_bf_lock['Estimated Unemployment Rate']

g_lock.columns = ['States','Unemployment Rate after lockdown','Unemployment Rate before lockdown']

g_lock.head(2)
Out[27]:
States Unemployment Rate after lockdown Unemployment Rate before lockdown
0 Andhra Pradesh 12.3975 9.4025
1 Assam 6.2450 6.2250
In [28]:
g_lock['percentage change in unemployment'] = round(g_lock['Unemployment Rate after lockdown'] - g_lock['Unemployment Rate before lockdown']/g_lock['Unemployment Rate before lockdown'],2)
In [29]:
plot_per = g_lock.sort_values('percentage change in unemployment')
In [30]:
# percentage change in unemployment after lockdown

fig = px.bar(plot_per, x='States',y='percentage change in unemployment',color='percentage change in unemployment',
            title='percentage change in Unemployment in each state after lockdown',template='ggplot2')

fig.show()
In [ ]: